Wind power is considered one of the cleanest, most environmentally friendly energy sources presently available, and wind turbines have gained increased attention in this regard. A modern wind turbine typically includes a tower, a generator, a gearbox, a nacelle, and one or more rotor blades. The rotor blades capture kinetic energy of wind using known foil principles. The rotor blades transmit the kinetic energy in the form of rotational energy so as to turn a low-speed main shaft coupling the rotor blades to a gearbox, or if a gearbox is not used, directly to the generator. For example, the generator may be coupled to the low-speed main shaft such that rotation of the shaft drives the generator. For instance, the generator may include a high-speed generator shaft rotatably coupled to the main shaft through the gearbox. The generator then converts the mechanical energy from the rotor to electrical energy that may be deployed to a utility grid.
In addition, modern wind turbines include a plurality of high-speed and low-speed bearings to provide rotation of the various components thereof. For example, the low-speed main shaft typically includes one or more main bearings mounted at a forward and rearward end thereof to allow the low-speed main shaft to rotate about an axis. Further, the gearbox generally includes a gearbox housing containing a plurality of gears (e.g., planet, ring and/or sun gears) connected via one or more planetary carriers and bearings, in addition to parallel gears connected via shafts and bearings, for converting the low speed, high torque input of the rotor shaft to a high speed, low torque output for the generator.
Detection of damaged components in a wind turbine (or any rotary machine) is essential in minimizing unplanned downtime of the turbine and increasing turbine availability. Current detection algorithms for gearbox gear damage (specifically, the ring gear, planet gear, and low-speed intermediate stage gear) do not consistently trend gear damage energy with enough separation from healthy gears. Lack of separation in the trends from healthy to damaged gears results in missed diagnosis of failing components and increased probability of false positive events for healthy components. Visual detection of failing gear sideband and harmonic energy patterns has proven successful in locating damaged components; however, this approach relies on the consistent manual inspection of the spectrums. Such inspection is inherently time consuming and can result in missed detection of failed components. In addition, although manual inspection methods have been utilized with success, such methods do not provide a scalable option and result in reduced monitoring efficiency.
For at least the aforementioned reasons, the detection of component damage of rotary machines has proven difficult to automate using traditional detection analytics and/or trending techniques. Accordingly, improved systems and methods for detecting damage in such rotary machines would be desired in the art.